Methods and applications of Raman microspectroscopy to single-cell analysis

Unveiling brain disorders using liquid biopsy and Raman spectroscopy

Brain disorders, including neurodegenerative diseases (NDs) and traumatic brain injury (TBI), present significant challenges in early diagnosis and intervention. Conventional imaging modalities, while valuable, lack the molecular specificity necessary for precise disease characterization. Compared to the study of conventional brain tissues, liquid biopsy, which focuses on blood, tear, saliva, and cerebrospinal fluid (CSF), also unveils a myriad of underlying molecular processes, providing abundant predictive clinical information. In addition, liquid biopsy is minimally- to non-invasive, and highly repeatable, offering the potential for continuous monitoring. Raman spectroscopy (RS), with its ability to provide rich molecular information and cost-effectiveness, holds great potential for transformative advancements in early detection and understanding the biochemical changes associated with NDs and TBI. Recent developments in Raman enhancement technologies and advanced data analysis methods have enhanced the applicability of RS in probing the intricate molecular signatures within biological fluids, offering new insights into disease pathology. This review explores the growing role of RS as a promising and emerging tool for disease diagnosis in brain disorders, particularly through the analysis of liquid biopsy. It discusses the current landscape and future prospects of RS in the diagnosis of brain disorders, highlighting its potential as a non-invasive and molecularly specific diagnostic tool.

Probing of nucleic acid compaction using low-frequency Raman spectroscopy

Compaction of nucleic acids, namely DNA and RNA, determines their functions and involvement in vital cell processes including transcription, replication, DNA repair and translation. However, experimental probing of the compaction of nucleic acids is not straightforward. In this study, we suggest an approach for this probing using low-frequency Raman spectroscopy. Specifically, we show theoretically, computationally and experimentally the quantifiable correlation between the low-frequency Raman intensity from nucleic acids, magnitude of thermal fluctuations of atomic positions, and the compaction state of biomolecules. Noteworthily, we highlight that the LF Raman intensity differs by an order of magnitude for different samples of DNA, and even for the same sample in the course of long-term storage. The feasibility of the approach is further shown by assessment of the DNA compaction in the nuclei of plant cells. We anticipate that the suggested approach will enlighten compaction of nucleic acids and their dynamics during the key processes of the cell life cycle and under various factors, facilitating advancement of molecular biology and medicine.

Combined Mutual Learning Net for Raman Spectral Microbial Strain Identification

Infectious diseases pose a significant threat to global health, yet traditional microbiological identification methods suffer from drawbacks, such as high costs and long processing times. Raman spectroscopy, a label-free and noninvasive technique, provides rich chemical information and has tremendous potential in fast microbial diagnoses. Here, we propose a novel Combined Mutual Learning Net that precisely identifies microbial subspecies. It demonstrated an average identification accuracy of 87.96% in an open-access data set with thirty microbial strains, representing a 5.76% improvement. 50% of the microbial subspecies accuracies were elevated by 1% to 46%, especially for E. coli 2 improved from 31% to 77%. Furthermore, it achieved a remarkable subspecies accuracy of 92.4% in the custom-built fiber-optical tweezers Raman spectroscopy system, which collects Raman spectra at a single-cell level. This advancement demonstrates the effectiveness of this method in microbial subspecies identification, offering a promising solution for microbiology diagnosis.

Light-sheet Raman tweezers for whole-cell biochemical analysis of functional red blood cells

Micro-Raman spectroscopy has emerged as one of the foremost techniques for analyzing biological cells in recent years due to its non-destructive nature and high spatial resolution. The development of optical tweezers has eased the research on biological cells as they confine living cells and organisms in the optical trap without causing much damage. Combining optical tweezers with Raman spectroscopy has opened a wide range of applications in the biomedical field as it facilitates biochemical analysis of biological samples by maintaining in-vivo conditions. Herein, we developed a light sheet-based optical tweezer that traps red blood cells (RBCs) at a very low power density spread across the whole cell, otherwise impossible with conventional optical tweezers. Furthermore, it is combined with micro-Raman spectroscopy to perform whole-cell biochemical analysis for the first time. Raman spectra of individual RBCs recorded under the line focal spot excitation are of superior quality and lack spectral signatures of photo-oxidation and heme aggregation, which is common in point focal spot excitations.

Multispectral Raman Differentiation of Malignant Skin Neoplasms In Vitro: Search for Specific Biomarkers and Optimal Wavelengths

Confocal scanning Raman and photoluminescence (PL) microspectroscopy is a structure-sensitive optical method that allows the non-invasive analysis of biomarkers in the skin tissue. We used it to perform in vitro diagnostics of different malignant skin neoplasms at several excitation wavelengths (532, 785 and 1064 nm). Distinct spectral differences were noticed in the Raman spectra of basal cell carcinoma (BCC) and squamous cell carcinoma (SCC), compared with healthy skin. Our analysis of Raman/PL spectra at the different excitation wavelengths enabled us to propose two novel wavelength-independent spectral criteria (intensity ratios for 1302 cm-1 and 1445 cm-1 bands, 1745 cm-1 and 1445 cm-1 bands), related to the different vibrational "fingerprints" of cell membrane lipids as biomarkers, which was confirmed by the multivariate curve resolution (MCR) technique. These criteria allowed us to differentiate healthy skin from BCC and SCC with sensitivity and specificity higher than 95%, demonstrating high clinical importance in the differential diagnostics of skin tumors.

Rapid Identification of Pathogens Causing Bloodstream Infections by Raman Spectroscopy and Raman Tweezers

The search for the "Holy Grail" in clinical diagnostic microbiology-a reliable, accurate, low-cost, real-time, easy-to-use method-has brought up several methods with the potential to meet these criteria. One is Raman spectroscopy, an optical, nondestructive method based on the inelastic scattering of monochromatic light. The current study focuses on the possible use of Raman spectroscopy for identifying microbes causing severe, often life-threatening bloodstream infections. We included 305 microbial strains of 28 species acting as causative agents of bloodstream infections. Raman spectroscopy identified the strains from grown colonies, with 2.8% and 7% incorrectly identified strains using the support vector machine algorithm based on centered and uncentred principal-component analyses, respectively. We combined Raman spectroscopy with optical tweezers to speed up the process and captured and analyzed microbes directly from spiked human serum. The pilot study suggests that it is possible to capture individual microbial cells from human serum and characterize them by Raman spectroscopy with notable differences among different species. IMPORTANCE Bloodstream infections are among the most common causes of hospitalizations and are often life-threatening. To establish an effective therapy for a patient, the timely identification of the causative agent and characterization of its antimicrobial susceptibility and resistance profiles are essential. Therefore, our multidisciplinary team of microbiologists and physicists presents a method that reliably, rapidly, and inexpensively identifies pathogens causing bloodstream infections-Raman spectroscopy. We believe that it might become a valuable diagnostic tool in the future. Combined with optical trapping, it offers a new approach where the microorganisms are individually trapped in a noncontact way by optical tweezers and investigated by Raman spectroscopy directly in a liquid sample. Together with the automatic processing of measured Raman spectra and comparison with a database of microorganisms, it makes the whole identification process almost real time.

Artificial intelligence-aided rapid and accurate identification of clinical fungal infections by single-cell Raman spectroscopy

Integrating artificial intelligence and new diagnostic platforms into routine clinical microbiology laboratory procedures has grown increasingly intriguing, holding promises of reducing turnaround time and cost and maximizing efficiency. At least one billion people are suffering from fungal infections, leading to over 1.6 million mortality every year. Despite the increasing demand for fungal diagnosis, current approaches suffer from manual bias, long cultivation time (from days to months), and low sensitivity (only 50% produce positive fungal cultures). Delayed and inaccurate treatments consequently lead to higher hospital costs, mobility and mortality rates. Here, we developed single-cell Raman spectroscopy and artificial intelligence to achieve rapid identification of infectious fungi. The classification between fungi and bacteria infections was initially achieved with 100% sensitivity and specificity using single-cell Raman spectra (SCRS). Then, we constructed a Raman dataset from clinical fungal isolates obtained from 94 patients, consisting of 115,129 SCRS. By training a classification model with an optimized clinical feedback loop, just 5 cells per patient (acquisition time 2 s per cell) made the most accurate classification. This protocol has achieved 100% accuracies for fungal identification at the species level. This protocol was transformed to assessing clinical samples of urinary tract infection, obtaining the correct diagnosis from raw sample-to-result within 1 h.

Micro-Raman spectroscopy of the light-harvesting pigments in Chlamydomonas reinhardtii under salinity stress

Microalgae are a rich source of carotenoids with enhanced yields during biotic or abiotic stresses, which often impose survival challenges on the cells. Using a non-invasive pigment profiling approach with micro-Raman spectroscopy, we have analyzed the effect of salinity stress on carotenoids in autotrophic Chlamydomonas reinhardtii. Raman spectral analysis of ν(C = C) mode indicates an increase in the carotenoids with lower conjugation length (lutein and zeaxanthin) compared to β-carotene, as the function of culture age and salinity stress, but especially when salinity stress was imposed in two-stage mode (stress imposed on 2nd day, D2_100, and 4th day, D4_100, during exponential phase). Population-scale heterogeneities in carotenoid Raman mode peak center, quantified with heterogeneity index (HI), were highest during the stationary phase of the cultures and under salinity stress. Although the Raman signal was obtained from a randomly selected small focal volume in the cell, a decrease in chlorophyll Raman mode intensities with age and salinity stress was well corroborated by single-cell population fraction measurements by microscopy. Raman intensity fluctuations (I_f) were high for both chlorophyll and carotenoid modes under salinity stress, which can arise due to variations in chlorophyll/carotenoid content and composition, or conformational changes in the pigments in C. reinhardtii cells. Interestingly, in all growth conditions, chlorophyll a Raman mode intensity was found to show a high correlation to that of β-carotene, pointing out a high degree of cooperativity in the light-harvesting complex pigments even during salinity stress. Thus, we demonstrate the usefulness of non-invasive pigment profiling with micro-Raman spectroscopy for developing an optimization for salinity stress conditions for high biomass yield and proper harvest time to obtain carotenoids with desired chemical composition.

Culture-Independent Raman Spectroscopic Identification of Bacterial Pathogens from Clinical Samples Using Deep Transfer Learning

The rapid identification of bacterial pathogens in clinical samples like blood, urine, pus, and sputum is the need of the hour. Conventional bacterial identification methods like culturing and nucleic acid-based amplification have limitations like poor sensitivity, high cost, slow turnaround time, etc. Raman spectroscopy, a label-free and noninvasive technique, has overcome these drawbacks by providing rapid biochemical signatures from a single bacterium. Raman spectroscopy combined with chemometric methods has been used effectively to identify pathogens. However, a robust approach is needed to utilize Raman features for accurate classification while dealing with complex data sets such as spectra obtained from clinical isolates, showing high sample-to-sample heterogeneity. In this study, we have used Raman spectroscopy-based identification of pathogens from clinical isolates using a deep transfer learning approach at the single-cell level resolution. We have used the data-augmentation method to increase the volume of spectra needed for deep-learning analysis. Our ResNet model could specifically extract the spectral features of eight different pathogenic bacterial species with a 99.99% classification accuracy. The robustness of our model was validated on a set of blinded data sets, a mix of cultured and noncultured bacterial isolates of various origins and types. Our proposed ResNet model efficiently identified the pathogens from the blinded data set with high accuracy, providing a robust and rapid bacterial identification platform for clinical microbiology.

Disentangling the Orientations of Spectrally Overlapping Transition Dipoles in Dense Dye Layers

The transition dipole orientations of dye assemblies in heterostructures have a crucial impact on the efficiency of novel optoelectronic devices such as organic thin-film transistors and light-emitting diodes. These devices are frequently based on heterojunctions and tandem structures featuring multiple optical transitions. Precise knowledge of preferred orientations, spatial order, and spatial variations is highly relevant. We present a fast and universal large-area screening method to determine the transition dipole orientations in dye assemblies with diffraction-limited spatial resolution. Moreover, our hyperspectral imaging approach disentangles the orientations of different chromophores. As a demonstration, we apply our technique to dye monolayers with two optical transitions sandwiched between two ultrathin silicate nanosheets. A comprehensive model for dipole orientation distributions in monolayers reveals a long-range orientational order and a strong correlation between the two transitions.

Early biomolecular changes in brain microvascular endothelial cells under Epstein-Barr virus influence: a Raman microspectroscopic investigation

The brain microvascular endothelial cells (ECs) play an important role in protecting the brain from hazardous pathogens. However, some viral pathogens can smartly modulate the endothelial pathways to gain entry inside the brain. Further, these viruses can cause endothelial dysfunction which could develop serious neurological ailments. Epstein-Barr virus (EBV), an oncogenic virus, has also been linked to various neurological disorders. The virus primarily infects epithelial and B cells, however, it also has a tendency to infect ECs and cause endothelial activation. However, the impact of EBV influence on ECs is still underexplored. Studying the early events of virus-mediated cellular modulation could help in understanding the virus' infection strategy or aftermath. Raman microspectroscopy has been widely utilized in biomedical sciences to decipher cellular changes. To understand the EBV-influenced EC modulation by studying intracellular biomolecular changes at early time points, we utilized the Raman microspectroscopy tool. We treated the ECs with EBV and acquired the Raman spectra at different time points (2, 4, 6, 12, 24 and 36 h) and different sites (nucleus and periphery) to check changes in Raman intensities associated with specific biomolecules. In the EBV-treated cells, the status of various biomolecules in terms of Raman intensities was observed to be altered compared with uninfected cells. Specifically, the cholesterol, polysaccharide, nucleotides, nucleic acid and proline moieties were altered at different time points. We also investigated the possible correlation between these molecules using molecular network analysis and observed various associated factors. These factors could be influenced by EBV to alter the associated biomolecular levels. Our study paves the pathway to study EBV infection in human brain microvascular ECs and highlights specific biomolecular alterations, which can be focused for further mechanistic investigations.

Lasers in Live Cell Microscopy

Due to their unique properties—coherent radiation, diffraction limited focusing, low spectral bandwidth and in many cases short light pulses—lasers play an increasing role in live cell microscopy. Lasers are indispensable tools in 3D microscopy, e.g., confocal, light sheet or total internal reflection microscopy, as well as in super-resolution microscopy using wide-field or confocal methods. Further techniques, e.g., spectral imaging or fluorescence lifetime imaging (FLIM) often depend on the well-defined spectral or temporal properties of lasers. Furthermore, laser microbeams are used increasingly for optical tweezers or micromanipulation of cells. Three exemplary laser applications in live cell biology are outlined. They include fluorescence diagnosis, in particular in combination with Förster Resonance Energy Transfer (FRET), photodynamic therapy as well as laser-assisted optoporation, and demonstrate the potential of lasers in cell biology and—more generally—in biomedicine.

Raman Spectroscopy—A Novel Method for Identification and Characterization of Microbes on a Single-Cell Level in Clinical Settings

Rapid and accurate identification of pathogens causing infections is one of the biggest challenges in medicine. Timely identification of causative agents and their antimicrobial resistance profile can significantly improve the management of infection, lower costs for healthcare, mitigate ever-growing antimicrobial resistance and in many cases, save lives. Raman spectroscopy was shown to be a useful—quick, non-invasive, and non-destructive —tool for identifying microbes from solid and liquid media. Modifications of Raman spectroscopy and/or pretreatment of samples allow single-cell analyses and identification of microbes from various samples. It was shown that those non-culture-based approaches could also detect antimicrobial resistance. Moreover, recent studies suggest that a combination of Raman spectroscopy with optical tweezers has the potential to identify microbes directly from human body fluids. This review aims to summarize recent advances in non-culture-based approaches of identification of microbes and their virulence factors, including antimicrobial resistance, using methods based on Raman spectroscopy in the context of possible use in the future point-of-care diagnostic process.

Raman Spectroscopy: In Vivo Application for Bone Evaluation in Oral Reconstructive (Regenerative) Surgery

The aim of this study was to evaluate the quality of the bone, revealing the different phases for calcified tissues independent of the medical history of the patient in relation to periodontitis by means of in vivo Raman spectroscopy. Raman spectroscopy measurements were performed in vivo during surgery and then ex vivo for the harvested bone samples for the whole group of patients (ten patients). The specific peaks for the Raman spectrum were traced for reference compounds (e.g., calcium phosphates) and bone samples. The variation in the intensity of the spectrum in relation to the specific bone constituents’ concentrations reflects the bone quality and can be strongly related with patient medical status (before dental surgery and after a healing period). Moreover, bone sample fluorescence is related to collagen content, enabling a complete evaluation of bone quality including a “quasi-quantification” of the healing process similar to the bone augmentation procedure. A complete evaluation of the processed spectra offers quantitative/qualitative information on the condition of the bone tissue. We conclude that Raman spectroscopy can be considered a viable investigation method for an in vivo and quick bone quality assessment during oral and periodontal surgery.

High-Speed Diagnosis of Bacterial Pathogens at the Single Cell Level by Raman Microspectroscopy with Machine Learning Filters and Denoising Autoencoders

Accurate and rapid identification of infectious bacteria is important in medicine. Raman microspectroscopy holds great promise in performing label-free identification at the single-cell level. However, due to the naturally weak Raman signal, it is a challenge to build extensive databases and achieve both accurate and fast identification. Here, we used signal-to-noise ratio (SNR) as a standard indicator for Raman data quality and performed bacterial identification using 11, 141 single-cell Raman spectra from nine bacterial strains. Subsequently, using two machine learning methods, a simple filter, and a neural network-based denoising autoencoder (DAE), we demonstrated 92% (simple filter using 1 s/cell spectra) and 84% (DAE using 0.1 s/cell spectra) identification accuracy. Our machine learning-aided Raman analysis paves the way for high-speed Raman microspectroscopic clinical diagnostics.

Raman spectroscopy-a tool for rapid differentiation among microbes causing urinary tract infections

Urinary tract infections belong to the most common infections in the world. Besides community-acquired infections, nosocomial infections pose a high risk especially for patients having indwelling catheters, undergoing urological surgeries or staying at hospital for prolonged time. They can be often complicated by antimicrobial resistance and/or biofilm formation. Therefore, a rapid diagnostic tool enabling timely identification of a causative agent and its susceptibility to antimicrobials is a need. Raman spectroscopy appears to be a suitable method that allows rapid differentiation among microbes and provides a space for further analyses, such as determination of capability of biofilm formation or antimicrobial susceptibility/resistance in tested strains. Our work here presents a possibility to differ among most common microbes causing urinary tract infections (belonging to 20 species). We tested 254 strains directly from colonies grown on Mueller-Hinton agar plates. The results show that it is possible to distinguish among the tested species using Raman spectroscopy, which proves its great potential for future use in clinical diagnostics. Moreover, we present here a pilot study of a real-time analysis and identification (in less than 10 min) of single microbial cells directly in urine employing optical tweezers combined with Raman spectroscopy.

Fast label-free recognition of NRBCs by deep-learning visual object detection and single-cell Raman spectroscopy

A computer-assisted and label-free method to quickly recognize the rare nucleated red blood cells by combining visual object detection with single-cell Raman spectroscopy.

Multimodal Scanning Microscope Combining Optical Coherence Tomography, Raman Spectroscopy and Fluorescence Lifetime Microscopy for Mesoscale Label-Free Imaging of Tissue

Multimodal optical imaging of tissue has significant potential to become an indispensable diagnostic tool in clinical pathology. Conventional bright-field microscopy provides contrast based on attenuation or reflectance of light, having no depth-related information and no molecular specificity. Recent developments in biomedical optics have introduced a variety of optical modalities, such as Raman spectroscopy (RS), fluorescence lifetime imaging microscopy (FLIM) of endogenous fluorophores, optical coherence tomography (OCT), and others, which provide a distinct characteristic, i.e., molecular, chemical, and morphological information, of the sample. To harvest the full analytical potential of those modalities, we have developed a novel multimodal imaging system, which allows the co-registered acquisition of OCT/FLIM/RS on a single device. The present implementation allows the investigation of biological tissues in the mesoscale range, 0.1-5 mm in a correlated manner. Due to the co-registered acquisition of the modalities, it is possible to directly compare and evaluate the corresponding information between the three modalities. Moreover, by additionally preparing and characterizing entire pathological hematoxylin and eosin (H&E) slides of head and neck biopsies, it is also possible to correlate the multimodal spectroscopic information to any location of the ground truth H&E information. To the best of our knowledge, this is the first development and implementation of a compact and clinically applicable multimodal scanning microscope, which combines OCT, FLIM, and RS together with the possibility for co-registering H&E information for a morpho-chemical tissue characterization and a correlation with the pathological ground truth (H&E) of the underlying signal origin directly in a clinical environment.

Antifungal activity of polymethyl methacrylate/Si3N4 composites against Candida albicans

Previous studies using gram-positive and -negative bacteria demonstrated that hydrolysis of silicon nitride (Si₃N₄) in aqueous suspensions elutes nitrogen and produces gaseous ammonia while buffering pH. According to immunochemistry assays, fluorescence imaging, and in situ Raman spectroscopy, we demonstrate here that the antipathogenic surface chemistry of Si₃N₄ can be extended to polymethylmethacrylate (PMMA) by compounding it with a minor fraction (~8 vol.%) of Si₃N₄ particles without any tangible loss in bulk properties. The hydrolytic products, which were eluted from partly exposed Si₃N₄ particles at the composite surface, exhibited fungicidal action against Candida albicans. Using a specific nitrative stress sensing dye and highly resolved fluorescence micrographs, we observed in situ congestion of peroxynitrite (ONOO^-) radicals in the mitochondria of the Candida cells exposed to the PMMA/Si₃N₄ composite, while these radicals were absent in the mitochondria of identical cells exposed to monolithic PMMA. These in situ observations suggest that the surface chemistry of Si₃N₄ mimics the antifungal activity of macrophages, which concurrently produce NO radicals and superoxide anions (O₂•^-) resulting in the formation of candidacidal ONOO^-. The fungicidal properties of PMMA/Si₃N₄ composites could be used in dental appliances to inhibit the uncontrolled growth of Candida albicans and ensuing candidiasis while being synergic with chemoprophylaxis. STATEMENT OF SIGNIFICANCE: In a follow-up of previous studies of gram-positive and gram-negative bacteria, we demonstrate here that the antipathogenic surface chemistry of Si₃N₄ could be extended to polymethylmethacrylate (PMMA) containing a minor fraction (~8 vol.%) of Si₃N₄ particles without tangible loss in bulk properties. Hydrolytic products eluted from Si₃N₄ particles at the composite surface exhibited fungicidal action against Candida albicans. Highly resolved fluorescence microscopy revealed congestion of peroxynitrite (ONOO^-) radicals in the mitochondria of the Candida cells exposed to the PMMA/Si₃N₄ composite, while radicals were absent in the mitochondria of identical cells exposed to monolithic PMMA. The fungicidal properties of PMMA/Si₃N₄ composites could be used in dental appliances to inhibit uncontrolled growth of Candida albicans and ensuing candidiasis in synergy with chemoprophylaxis.

Fast confocal Raman imaging via context-aware compressive sensing

Raman hyperspectral imaging is a powerful method to obtain detailed chemical information about a wide variety of organic and inorganic samples noninvasively and without labels. However, due to the weak, nonresonant nature of spontaneous Raman scattering, acquiring a Raman imaging dataset is time-consuming and inefficient. In this paper we utilize a compressive imaging strategy coupled with a context-aware image prior to improve Raman imaging speed by 5- to 10-fold compared to classic point-scanning Raman imaging, while maintaining the traditional benefits of point scanning imaging, such as isotropic resolution and confocality. With faster data acquisition, large datasets can be acquired in reasonable timescales, leading to more reliable downstream analysis. On standard samples, context-aware Raman compressive imaging (CARCI) was able to reduce the number of measurements by ∼85% while maintaining high image quality (SSIM >0.85). Using CARCI, we obtained a large dataset of chemical images of fission yeast cells, showing that by collecting 5-fold more cells in a given experiment time, we were able to get more accurate chemical images, identification of rare cells, and improved biochemical modeling. For example, applying VCA to nearly 100 cells' data together, cellular organelles were resolved that were not faithfully reconstructed by a single cell's dataset.

Advances in stimulated Raman scattering imaging for tissues and animals

Stimulated Raman scattering (SRS) microscopy has emerged in the last decade as a powerful optical imaging technology with high chemical selectivity, speed, and subcellular resolution. Since the invention of SRS microscopy, it has been extensively employed in life science to study composition, structure, metabolism, development, and disease in biological systems. Applications of SRS in research and the clinic have generated new insights in many fields including neurobiology, tumor biology, developmental biology, metabolomics, pharmacokinetics, and more. Herein we review the advances and applications of SRS microscopy imaging in tissues and animals, as well as envision future applications and development of SRS imaging in life science and medicine.

The Use of Raman Spectroscopy to Monitor Metabolic Changes in Stressed Metschnikowia sp. Yeasts

Raman spectroscopy is a universal method designed for the analysis of a wide range of physical, chemical and biological systems or various surfaces. This technique is suitable to monitor various components of cells, tissues or microorganisms. The advantages include very fast non-contact and non-destructive analysis and no or minimal need for sample treatment. The yeasts Metschnikowia can be considered as industrially usable producers of pulcherrimin or single-cell lipids, depending on cultivation conditions and external stress. In the present study, Raman spectroscopy was used as an effective tool to identify both pulcherrimin and lipids in single yeast cells. The analysis of pulcherrimin is very demanding; so far, there is no optimal procedure to analyze or identify this pigment. Based on results, the strong dependence of pulcherrimin production on the ferric ion concentration was found with the highest yield in media containing 0.1 g/L iron. Further, production of lipids in Metschnikowia cells was studied at different temperatures and C:N ratios, using Raman spectroscopy to follow fatty acids composition, under different regimes, by monitoring the iodine number. The results of Raman spectroscopy were comparable with the fatty acid analysis obtained by gas chromatography. This study therefore supported use of Raman spectroscopy for biotechnological applications as a simple tool in the identification and analysis both the pulcherrimin and microbial lipids. This method provides a quick and relatively accurate estimation of targeted metabolites with minimal sample modification and allows to monitor metabolic changes over time of cultivation.

Using intracellular plasmonics to characterize nanomorphology in human cells

Determining the characteristics and localization of nanoparticles inside cells is crucial for nanomedicine design for cancer therapy. Hyperspectral imaging is a fast, straightforward, reliable, and accurate method to study the interactions of nanoparticles and intracellular components. With a hyperspectral image, we could collect spectral information consisting of thousands of pixels in a short time. Using hyperspectral images, in this work, we developed a label-free technique to detect nanoparticles in different regions of the cell. This technique is based on plasmonic shifts taking place during the interaction of nanoparticles with the surrounding medium. The unique optical properties of gold nanoparticles, localized surface plasmon resonance bands, are influenced by their microenvironment. The LSPR properties of nanoparticles, hence, could provide information on regions in which nanoparticles are distributed. To examine the potential of this technique for intracellular detection, we used three different types of gold nanoparticles: nanospheres, nanostars and Swarna Bhasma (SB), an Indian Ayurvedic/Sidha medicine, in A549 (human non-small cell lung cancer) and HepG2 (human hepatocellular carcinoma) cells. All three types of particles exhibited broader and longer bands once they were inside cells; however, their plasmonic shifts could change depending on the size and morphology of particles. This technique, along with dark-field images, revealed the uniform distribution of nanospheres in cells and could provide more accurate information on their intracellular microenvironment compared to the other particles. The region-dependent optical responses of nanoparticles in cells highlight the potential application of this technique for subcellular diagnosis when particles with proper size and morphology are chosen to reflect the microenvironment effects properly.

Morpho-molecular ex vivo detection and grading of non-muscle-invasive bladder cancer using forward imaging probe based multimodal optical coherence tomography and Raman spectroscopy

Non-muscle-invasive bladder cancer affects millions of people worldwide, resulting in significant discomfort to the patient and potential death. Today, cystoscopy is the gold standard for bladder cancer assessment, using white light endoscopy to detect tumor suspected lesion areas, followed by resection of these areas and subsequent histopathological evaluation. Not only does the pathological examination take days, but due to the invasive nature, the performed biopsy can result in significant harm to the patient. Nowadays, optical modalities, such as optical coherence tomography (OCT) and Raman spectroscopy (RS), have proven to detect cancer in real time and can provide more detailed clinical information of a lesion, e.g. its penetration depth (stage) and the differentiation of the cells (grade). In this paper, we present an ex vivo study performed with a combined piezoelectric tube-based OCT-probe and fiber optic RS-probe imaging system that allows large field-of-view imaging of bladder biopsies, using both modalities and co-registered visualization, detection and grading of cancerous bladder lesions. In the present study, 119 examined biopsies were characterized, showing that fiber-optic based OCT provides a sensitivity of 78% and a specificity of 69% for the detection of non-muscle-invasive bladder cancer, while RS, on the other hand, provides a sensitivity of 81% and a specificity of 61% for the grading of low- and high-grade tissues. Moreover, the study shows that a piezoelectric tube-based OCT probe can have significant endurance, suitable for future long-lasting in vivo applications. These results also indicate that combined OCT and RS fiber probe-based characterization offers an exciting possibility for label-free and morpho-chemical optical biopsies for bladder cancer diagnostics.

Mammalian cell and tissue imaging using Raman and coherent Raman microscopy

Direct imaging of metabolism in cells or multicellular organisms is important for understanding many biological processes. Raman scattering (RS) microscopy, particularly, coherent Raman scattering (CRS) such as coherent anti-Stokes Raman scattering (CARS) and stimulated Raman scattering (SRS), has emerged as a powerful platform for cellular imaging due to its high chemical selectivity, sensitivity, and imaging speed. RS microscopy has been extensively used for the identification of subcellular structures, metabolic observation, and phenotypic characterization. Conjugating RS modalities with other techniques such as fluorescence or infrared (IR) spectroscopy, flow cytometry, and RNA-sequencing can further extend the applications of RS imaging in microbiology, system biology, neurology, tumor biology and more. Here we overview RS modalities and techniques for mammalian cell and tissue imaging, with a focus on the advances and applications of CARS and SRS microscopy, for a better understanding of the metabolism and dynamics of lipids, protein, glucose, and nucleic acids in mammalian cells and tissues. This article is categorized under: Laboratory Methods and Technologies > Imaging Biological Mechanisms > Metabolism Analytical and Computational Methods > Analytical Methods.

Enhanced single-cell encapsulation in microfluidic devices: From droplet generation to single-cell analysis

Single-cell analysis to investigate cellular heterogeneity and cell-to-cell interactions is a crucial compartment to answer key questions in important biological mechanisms. Droplet-based microfluidics appears to be the ideal platform for such a purpose because the compartmentalization of single cells into microdroplets offers unique advantages of enhancing assay sensitivity, protecting cells against external stresses, allowing versatile and precise manipulations over tested samples, and providing a stable microenvironment for long-term cell proliferation and observation. The present Review aims to give a preliminary guidance for researchers from different backgrounds to explore the field of single-cell encapsulation and analysis. A comprehensive and introductory overview of the droplet formation mechanism, fabrication methods of microchips, and a myriad of passive and active encapsulation techniques to enhance single-cell encapsulation efficiency were presented. Meanwhile, common methods for single-cell analysis, especially for long-term cell proliferation, differentiation, and observation inside microcapsules, are briefly introduced. Finally, the major challenges faced in the field are illustrated, and potential prospects for future work are discussed.

Detection and time-tracking activation of a photosensitiser on live single colorectal cancer cells using Raman spectroscopy

Raman spectroscopy has been used to observe uptake, metabolism and response of single-cells to drugs. Photodynamic therapy is based on the use of light, a photosensitiser and oxygen to destroy tumour tissue. Here, we used single-cell Raman spectroscopy to study the uptake and intracellular degradation of a novel photosensitiser with a diphenylacetylene structure, DC473, in live single-cells from colorectal adenocarcinoma cell lines SW480, HT29 and SW620. DC473 was seen to predominantly accumulate in lipid droplets, showing higher accumulation in HT29 and SW620 cells than in SW480 cells, with a broader DC473 peak shifted to higher wavenumbers. DC473 activation and effects were tracked on live single-cells for 5 minutes. Upon exposure to UV light, the DC473 signal intensity dropped, with remaining DC473 shifting towards higher wavenumbers and widening, with a lifetime of approximately 50 seconds. Morphologically, SW480 and SW620 cells showed changes upon photodynamic therapy, whereas HT29 cells showed no changes. Morphological changes correlated with higher remaining DC473 signal after UV exposure. Our research suggests that DC473 forms aggregates within the cells that disaggregate following activation, showing the potential of Raman spectroscopy for the study of time-dependent single-cell pharmacodynamics.

Raman Cryomicroscopic Imaging and Sample Holder for Spectroscopic Subzero Temperature Measurements

Raman spectroscopy has been gaining in popularity for noninvasive analysis of single cells. Raman spectra and images deliver meaningful information regarding the biochemical, biophysical, and structural properties of cells in various states. Low-temperature Raman spectroscopy has been applied to verify the presence of ice inside a frozen cell and to illustrate the distribution of both penetrating and non-penetrating cryoprotectants. This chapter delineates Raman cryomicroscopic imaging of single cells as well as sample handling for spectroscopic measurements at subzero temperature. The experimental setup is depicted with a special emphasis on a custom-built temperature-controlled cooling stage. The use of Raman cryomicroscopic imaging is demonstrated using Jurkat cells cryopreserved in a sucrose solution. Moreover, strategies for determining intracellular ice formation (IIF) and analysis of sucrose partitioning across the cell membrane are presented.

Molecular profiling of lipid droplets inside HuH7 cells with Raman micro-spectroscopy

Raman imaging has become an attractive technology in molecular biology because of its ability to detect multiple molecular components simultaneously without labeling. Two major limitations in accurately accounting for spectral features, viz., background removal and spectral unmixing, have been overcome by employing a modified and effective routine in multivariate curve resolution (MCR). With our improved strategy, we have spectrally isolated seven structurally specific biomolecules without any post-acquisition spectral treatments. Consequently, the isolated intensity profiles reflected concentrations of corresponding biomolecules with high statistical accuracy. Our study reveals the changes in the molecular composition of lipid droplets (LDs) inside HuH7 cells and its relation to the physiological state of the cell. Further, we show that the accurate separation of spectral components permits analysis of structural modification of molecules after cellular uptake. A detailed discussion is presented to highlight the potential of Raman spectroscopy with MCR in semi-quantitative molecular profiling of living cells.

Samuel, Miyaoka et al. investigate the changes in the molecular composition of lipid droplets inside HuH7 cells and its relation to the physiological state of the cell, using Raman spectroscopy and multivariate curve resolution. This study underscores the importance of separation of spectral components in semi-quantitative molecular profiling of living cells.

Combination of an Artificial Intelligence Approach and Laser Tweezers Raman Spectroscopy for Microbial Identification

Raman spectroscopy is a nondestructive, label-free, highly specific approach that provides the chemical information on materials. Thus, it is suitable to be used as an effective analytical tool to characterize biological samples. Here we introduce a novel method that uses artificial intelligence to analyze biological Raman spectra and identify the microbes at a single-cell level. The combination of a framework of convolutional neural network (ConvNet) and Raman spectroscopy allows the extraction of the Raman spectral features of a single microbial cell and then categorizes cells according to their spectral features. As the proof of concept, we measured Raman spectra of 14 microbial species at a single-cell level and constructed an optimal ConvNet model using the Raman data. The average accuracy of classification by ConvNet is 95.64 ± 5.46%. Meanwhile, we introduced an occlusion-based Raman spectra feature extraction to visualize the weights of Raman features for distinguishing different species.

Raman spectroscopy-based approach to study the female gamete

In the last years, an increasing interest has emerged on the development of new non-invasive methods for the assessment of oocyte quality in order to improve outcomes of assisted reproductive technologies (ARTs) either in medical or veterinary fields. Raman microspectroscopy (RMS) has been proposed as a promising tool for the examination of the mammalian female gamete and identification of markers of its developmental competence. This technique provides a unique spectral fingerprint indicative of molecular composition of the cell and allows probing subcellular compartments. Studies have been carried out analysing by RMS fixed or living oocytes derived from different animal models. RMS imaging has been successfully applied to discriminate the biochemical changes of the global molecular architecture of mouse oocytes at different stages of maturation and those occurring in different conditions of maturation and oocyte aging. RMS can also detect modifications of specific structural components, including the oocyte zona pellucida and F-actin subcortical cytoskeleton in fresh sheep oocytes and those underwent to vitrification procedures. Finally, the recent application of Coherent anti-Stokes Raman scattering (CARS) microscopy for examination of oocyte lipid component will be briefly discussed. CARS overcomes some limits of RMS providing vibrational and spectral information with higher sensitivity, spatial resolution which is ideal to study living oocytes. This review summarizes the research on RMS approaches for oocyte evaluation showing the high potential use, current limitations and new improvements.

Parallelized shifted-excitation Raman difference spectroscopy for fluorescence rejection in a temporary varying system

A fluorescence background is one of the common interference factors of the Raman spectroscopic analysis in the biology field. Shifted-excitation Raman difference spectroscopy (SERDS), in which a slow (typically 1 Hz) modulation to excitation wavelength is coupled with a sequential acquisition of alternating shifted-excitation spectra, has been used to separate Raman scattering from excitation-shift insensitive background. This sequential method is susceptible to spectral change and thus is limited only to stable samples. We incorporated a fast laser modulation (200 Hz) and a mechanical streak camera into SERDS to effectively parallelize the SERDS measurement in a single exposure. The developed system expands the scope of SERDS to include temporary varying system. The proof of concept is demonstrated using highly fluorescent samples, including living algae. Quantitative performance in fluorescence rejection and the robustness of the method to the dynamic spectral change during the measurement are manifested.

Phenotypic Image Analysis Software Tools for Exploring and Understanding Big Image Data from Cell-Based Assays

Phenotypic image analysis is the task of recognizing variations in cell properties using microscopic image data. These variations, produced through a complex web of interactions between genes and the environment, may hold the key to uncover important biological phenomena or to understand the response to a drug candidate. Today, phenotypic analysis is rarely performed completely by hand. The abundance of high-dimensional image data produced by modern high-throughput microscopes necessitates computational solutions. Over the past decade, a number of software tools have been developed to address this need. They use statistical learning methods to infer relationships between a cell's phenotype and data from the image. In this review, we examine the strengths and weaknesses of non-commercial phenotypic image analysis software, cover recent developments in the field, identify challenges, and give a perspective on future possibilities.

New perspectives for viability studies with high-content analysis Raman spectroscopy (HCA-RS)

Raman spectroscopy has been widely used in clinical and molecular biological studies, providing high chemical specificity without the necessity of labels and with little-to-no sample preparation. However, currently performed Raman-based studies of eukaryotic cells are still very laborious and time-consuming, resulting in a low number of sampled cells and questionable statistical validations. Furthermore, the approach requires a trained specialist to perform and analyze the experiments, rendering the method less attractive for most laboratories. In this work, we present a new high-content analysis Raman spectroscopy (HCA-RS) platform that overcomes the current challenges of conventional Raman spectroscopy implementations. HCA-RS allows sampling of a large number of cells under different physiological conditions without any user interaction. The performance of the approach is successfully demonstrated by the development of a Raman-based cell viability assay, i.e., the effect of doxorubicin concentration on monocytic THP-1 cells. A statistical model, principal component analysis combined with support vector machine (PCA-SVM), was found to successfully predict the percentage of viable cells in a mixed population and is in good agreement to results obtained by a standard cell viability assay. This study demonstrates the potential of Raman spectroscopy as a standard high-throughput tool for clinical and biological applications.

Raman Analysis of Tear Fluid Alteration Following Contact Lense Use

Tear fluid is a heterogeneous solution containing mainly proteins, lipids, mucins and electrolytes, which regulates the physiology of the human eye. The complex composition of tears can be altered in the presence of eye inflammations. The use of contact lenses is one of the most frequent causes of inflammatory responses of the eye, with the related discomfort often causing the wearer to give up using them. In this paper, we exploit the potentiality of Raman Spectroscopy to analyse the biochemical changes in tear fluid in a contact lens wearer. In particular, we analysed the tear fluid collected from a volunteer as a function of the wearing time for two types of monthly contact lenses (Hydrogel and Si-Hydrogel). Our experimental results show an alteration of the relative concentrations of proteins and lipids in both of the analysed cases. More importantly, our results highlight the diagnostic sensitivity of Raman analysis to select the proper contact lens type for each wearer and optimise the lens wearing conditions.

Nondestructive Identification and Accurate Isolation of Single Cells through a Chip with Raman Optical Tweezers

Raman optical tweezers (ROT) as a label-free technique plays an important role in single-cell study such as heterogeneity of tumor and microbial cells. Herein we designed a chip utilizing ROT to isolate a specific single cell. The chip was made from a polydimethylsiloxane (PDMS) slab and formed into a gourd-shaped reservoir with a connected channel on a cover glass. On the chip an individual cell could be isolated from a cell crowd and then extracted with ∼0.5 μL of phosphate-buffered saline (PBS) via pipet immediately after Raman spectral measurements of the same cell. As verification, we separated four different type of cells including BGC823 gastric cancer cells, erythrocytes, lymphocytes, and E. coli cells and quantifiably characterized the heterogeneity of the cancer cells, leukocyte subtype, and erythrocyte status, respectively. The average time of identifying and isolating a specific cell was 3 min. Cell morphology comparison and viability tests showed that the successful rate of single-cell isolation was about 90%. Thus, we believe our platform could further couple other single-cell techniques such as single-cell sequencing and become a multiperspective analytical approach at the level of a single cell.

Cellular Imaging Using Stimulated Raman Scattering Microscopy

Cellular imaging is an active area of research that enables researchers to monitor cellular dynamics, as well as responses to various external stimuli (physiological stress, exogenous compounds, etc.). Stimulated Raman scattering (SRS) microscopy is one popular experimental tool used to image cells, largely because of its chemical specificity, high spatial resolution, and high image acquisition speed. In this Perspective, the theoretical background and experimental implementation of SRS microscopy are discussed and recent developments in the field of cellular imaging with SRS are highlighted and summarized.

Single-Cell and Time-Resolved Profiling of Intracellular Salmonella Metabolism in Primary Human Cells

The intracellular pathogen Salmonella enterica has evolved an array of traits for propagation and invasion of the intestinal layers. It remains largely elusive how Salmonella adjusts its metabolic states to survive inside immune host cells. In this study, single-cell Raman biotechnology combined with deuterium isotope probing (Raman-DIP) have been applied to reveal metabolic changes of the typhoidal Salmonella Typhi Ty2, the nontyphoidal Salmonella Typhimurium LT2, and a clinical isolate Typhimurium D23580. By initially labeling the Salmonella strains with deuterium, we employed reverse labeling to track their metabolic changes in the time-course infection of THP-1 cell line, human monocyte-derived dendritic cells (MoDCs) and macrophages (Mf). We found that, in comparison with a noninvasive serovar, the invasive Salmonella strains Ty2 and D23580 have downregulated metabolic activity inside human macrophages and dendritic cells and used lipids as alternative carbon source, perhaps a strategy to escape from the host immune response. Proteomic analysis using high sensitivity mass spectrometry validated the findings of Raman-DIP analysis.

Identification of ability to form biofilm in Candida parapsilosis and Staphylococcus epidermidis by Raman spectroscopy

Aim: Finding rapid, reliable diagnostic methods is a big challenge in clinical microbiology. Raman spectroscopy is an optical method used for multiple applications in scientific fields including microbiology. This work reports its potential in identifying biofilm positive strains of Candida parapsilosis and Staphylococcus epidermidis. Materials & methods: We tested 54 S. epidermidis strains (23 biofilm positive, 31 negative) and 51 C. parapsilosis strains (27 biofilm positive, 24 negative) from colonies on Mueller-Hinton agar plates, using Raman spectroscopy. Results: The accuracy was 98.9% for C. parapsilosis and 96.1% for S. epidermidis. Conclusion: The method showed great potential for identifying biofilm positive bacterial and yeast strains. We suggest that Raman spectroscopy might become a useful aid in clinical diagnostics.

Label-free tracking of nanosized graphene oxide cellular uptake by confocal Raman microscopy

Graphene oxide (GO), a partially oxidized two-dimensional allotrope of carbon, is an attractive nanocarrier for cancer diagnostics and therapy. The nanometer-sized GO is known to permeate cell membranes. Herein we studied the cellular uptake pathways of GO nanoflakes by cancer and non-cancerous cell lines. By employing confocal Raman imaging, we were able to track the GO cellular uptake in living cells (C33 and MDCK) without any additional fluorescent or plasmonic labels. This specific progress in label-free Raman imaging of GO facilitates the monitoring of nanoflakes at the cellular level.

Raman tweezers microspectroscopy of circa 100 nm extracellular vesicles

The technique of Raman tweezers microspectroscopy (RTM) for the global biomolecular content characterization of a single extracellular vesicle (EV) or a small number of EVs or other nanoscale bioparticles in an aqueous dispersion in the difficult-to-access size range of near 100 nm is described in detail. The particularities and potential of RTM are demonstrated using the examples of DOPC liposomes, exosomes from human urine and rat hepatocytes, and a mixed sample of the transfection reagent FuGENE in diluted DNA solution. The approach of biomolecular component analysis for the estimation of the main biomolecular contributions (proteins, lipids, nucleic acids, carotenoids, etc.) is proposed and discussed. Direct Raman evidence for strong intra-sample biomolecular heterogeneity of individual optically trapped EVs, due to variable contributions from nucleic acids and carotenoids in some preparations, is reported. On the basis of the results obtained, we are making an attempt to convince the scientific community that RTM is a promising method of single-EV research; to our knowledge, it is the only technique available at the moment that provides unique information about the global biomolecular composition of a single vesicle or a small number of vesicles, thus being capable of unravelling the high diversity of EV subpopulations, which is one of the most significant urgent challenges to overcome. Possible RTM applications include, among others, searching for DNA biomarkers, cancer diagnosis, and discrimination between different subpopulations of EVs, lipid bodies, protein aggregates and viruses.

Dynamic characterization of drug resistance and heterogeneity of the gastric cancer cell BGC823 using single-cell Raman spectroscopy

Drug resistance and heterogeneous characteristics of human gastric carcinoma cells (BGC823) under the treatment of paclitaxel (PTX) were investigated using single-cell Raman spectroscopy (RS). RS of normal and drug-resistant BGC823 cells (DR-BGC823) were collected and analyzed using arithmetic, statistic and individual spectrum analysis. The dynamic effects of paclitaxel (PTX) in normal and DR-BGC823 cells were evaluated dynamically. The RS intensity changed with PTX over time and produced distinct different results for the two types of cells. The average RS intensities of the normal BGC823 cells initially decreased and then increased under PTX treatment after 24 hours. In contrast, upon exposure to PTX, the average intensity of the DR-BGC823 cells initially increased within 12 hours and then gradually decreased and approached a steady state. The temporal variation of the typical component in the cells was analyzed by comparing the ratios between Raman bands. More importantly, the heterogeneous characteristics of the BGC823 cells under PTX treatment were quantified and clustered using hierarchical trees combined with RS intensity changes. The 'outlier' cells related to drug resistance were discriminated. The heterogeneity of the normal BGC823 cells under drug treatment gradually appeared over time, and was evaluated with the eigenvalues of principal component analysis (PCA). Our study indicates that single-cell RS may be useful in systematically and dynamically characterizing the drug response of cancer cells at the single-cell level.

Biomolecular Component Analysis of Phospholipids Composition in Live HeLa Cells

The alteration of the phospholipid composition within the cell, in particular the ratio between saturated and unsaturated fatty acids, can serve as an important biomarker to prognosis of the disease progression (e.g., fatty-liver disease, prostate cancer, or neurodegenerative disorders). Major techniques for lipid analysis in biological samples require a lipid extraction procedure that is not compatible with live cell studies. To address this challenge, we apply microRaman-Biomolecular Component Analysis (BCA) for comparative analysis of phospholipid composition and sensing the saturation degree of fatty acid lipid chain in live HeLa cells and lipids extracted from HeLa cells. After processing raw Raman data, acquired in lipid droplets (LDs) free cytoplasmic area, LDs and extracted lipids with BCA, the lipid component was isolated. Despite the similarity in general profiles of processed Raman spectra acquired in live cells and extracted lipids, some clear differences that reflect diversity in their phospholipids composition were revealed. Furthermore, using the direct relation between the number of double bonds in the fatty acid chain and the intensity ratio of the corresponding Raman bands, the saturation degree of fatty acids was estimated.

The Impact of Antioxidants from the Diet on Breast Cancer Cells Monitored by Raman Microspectroscopy

Background:

The impact of the ubiquitous dietary phenolic compound p-coumaric acid on human breast cancer cells was assessed, through a multidisciplinary approach: Combined biological assays for cytotoxicity evaluation and biochemical profiling by Raman microspectroscopic analysis in cells. </P><P> Methods: Para-coumaric acid was shown to exert in vitro chemoprotective and antitumor activities, depending on the concentration and cell line probed: a significant anti-invasive ability was detected for the triple-negative MDA-MB-231 cells, while a high pro-oxidant effect was found for the estrogen- dependent MCF-7 cells. A striking cell selectivity was obtained, with a more noticeable outcome on the triple-negative MDA-MB-231 cell line.

Results:

The main impact on the cellular biochemical profile was verified to be on proteins and lipids, thus justifying the compound´s anti-invasive effect and chemoprotective ability.

Conclusion:

p-Coumaric acid was thus shown to be a promising chemoprotective/chemotherapeutic agent, particularly against the low prognosis triple-negative human breast adenocarcinoma.

Anti-Cancer Drug Sensitivity Assay with Quantitative Heterogeneity Testing Using Single-Cell Raman Spectroscopy

A novel anti-cancer drug sensitivity testing (DST) approach was developed based on in vitro single-cell Raman spectrum intensity (RSI). Generally, the intensity of Raman spectra (RS) for a single living cell treated with drugs positively relates to the sensitivity of the cells to the drugs. In this study, five cancer cell lines (BGC 823, SGC 7901, MGC 803, AGS, and NCI-N87) were exposed to three cytotoxic compounds or to combinations of these compounds, and then they were evaluated for their responses with RSI. The results of RSI were consistent with conventional DST methods. The parametric correlation coefficient for the RSI and Methylthiazolyl tetrazolium assay (MTT) was 0.8558 ± 0.0850, and the coefficient of determination was calculated as R² = 0.9529 ± 0.0355 for fitting the dose⁻response curve. Moreover, RSI data for NCI-N87 cells treated by trastuzumab, everolimus (cytostatic), and these drugs in combination demonstrated that the RSI method was suitable for testing the sensitivity of cytostatic drugs. Furthermore, a heterogeneity coefficient H was introduced for quantitative characterization of the heterogeneity of cancer cells treated by drugs. The largest possible variance between RSs of cancer cells were quantitatively obtained using eigenvalues of principal component analysis (PCA). The ratio of H between resistant cells and sensitive cells was greater than 1.5, which suggested the H-value was effective to describe the heterogeneity of cancer cells. Briefly, the RSI method might be a powerful tool for simple and rapid detection of the sensitivity of tumor cells to anti-cancer drugs and the heterogeneity of their responses to these drugs.

Pulse length variation causing spectral distortions in OPO-based hyperspectral coherent Raman scattering microscopy

Picosecond optical parametric oscillators (OPOs) with broad wavelength tunability are frequently used as light sources in hyperspectral coherent Raman scattering (CRS) microscopy. We investigate how changes in the pulse length during OPO wavelength tuning of the pump beam affect hyperspectral CRS imaging. We find that significant distortions of the resulting CRS spectra occur if the OPO is operated without monitoring pulse length variations. By utilizing a custom-written MATLAB based control program to counteract changes in pulse length, normalized and reproducible data sets can be acquired. We demonstrate this by comparing hyperspectral data obtained from pure substances, as well as relevant biological specimens.

In vivo detection of drug-induced apoptosis in tumors using Raman spectroscopy

We describe a label-free approach based on Raman spectroscopy, to study drug-induced apoptosis in vivo. Spectral-shifts at wavenumbers associated with DNA, proteins, lipids, and collagen have been identified on breast and melanoma tumor tissues. These findings may enable a new analytical method for rapid readout of drug-therapy with miniaturized probes.